MegaTrans – human transporter machine learning models

MegaTrans — 人类运输机机器学习模型

• 批准号: 10546264
• 负责人: Nathan J Cherrington
• 金额: $ 86.48万
• 依托单位: COLLABORATIONS PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546264
• 关键词: 3-Dimensional; Agrochemicals; Algorithms; Angiotensin-Converting Enzyme Inhibitors; Antiviral Agents; Arizona; Bayesian Method; Bayesian learning; Behavior; Biological Assay; Blood-Testis Barrier; COVID-19 treatment; CRISPR/Cas technology; Chemistry; Client; Clinical; Code; Collaborations; Collection; Computer Models; Computer software; Consult; Data; Data Set; Databases; Decision Trees; Descriptor; Docking; Drug Design; Drug Industry; Drug Interactions; Drug Modelings; Evaluation; Family; Fee-for-Service Plans; Fingerprint; Foundations; Graph; Hela Cells; Hepatocyte; Human; In Vitro; Industry; International; Intuition; Investments; Learning; Libraries; Licensing; Ligands; Literature; Machine Learning; Mediating; Methods; Modeling; Molecular; Natural Products; Nucleoside Transporter; Online Systems; Organ; Output; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Process; Property; PubChem; Public Domains; Pythons; Receiver Operating Characteristics; Reporting; Resources; Risk; Seminal fluid; Site; Software Tools; Structure; Structure-Activity Relationship; System; Testing; Toxic Environmental Substances; Toxic effect; Training; Trees; Universities; Uridine; Validation; Vendor; Virus; Visualization; Work; Xenobiotics; base; clinically relevant; computerized tools; consumer product; data curation; deep learning; design; drug candidate; drug discovery; drug disposition; high throughput screening; improved; in vitro testing; in vivo; inhibitor; inhibitor therapy; interest; long short term memory; machine learning algorithm; machine learning method; machine learning model; member; model building; molecular shape; molnupiravir; neural network; novel therapeutics; pharmacophore; predictive modeling; prospective; prototype; random forest; remdesivir; side effect; software development; tool; tool development; uptake; web app

Summary Being able to predict interactions with important human transporters would be of value to new drug design to avoid compounds that interact with them and cause undesirable side effects. Conversely, some drug transporters can be used for targeting molecules to specific organs and this may have considerable utility. Understanding the interactions of novel drugs, natural products and environmental toxicants and their interactions with an array of such transporters is, therefore, important for several industries, as well as from a regulatory perspective (e.g. FDA, EPA and EMA). Being able to predict such interactions in a fast and reliable manner effectively requires using computational approaches and learning from in vitro data, the latter a resource that is rapidly growing. Over the past 20 years, we have been at the forefront of applying different machine learning approaches to modeling drug transporters and, in many cases, developing datasets for transporters for which there was scant available data. We now propose doing this for several transporters that may be important for drug discovery. In Phase I we focused on OATP1B1 (SLCO1B1), which is an uptake transporter largely restricted to the sinusoidal aspect of hepatocytes where it mediates transport of a variety of structurally unrelated compounds, including members of several clinically important drug families (incl. statins, sartans and angiotensin converting enzyme (ACE) inhibitors). We tested 476 drugs against one substrate in vitro. We then curated these data and built machine learning models using multiple machine learning methods as well as model evaluation metrics. This enabled us to develop models for integration in a web-based software tool called MegaTrans® that enables the user to input their own compound structures and generate predictions for interactions with transporter/s of interest, as well as visualize the similarity to the training set of each model using several different visualization methods. In addition, during Phase I we also performed preliminary data curation, model building and validation for two equilibrative nucleoside transporters (ENTs), ENT1 and ENT2, that are present at the blood testes barrier (BTB), where they can facilitate drug disposition (e.g. for antivirals, thereby potentially eliminating a sanctuary site for viruses detectable in semen). We generated Bayesian and pharmacophore models and used these to predict numerous compounds that were then tested in vitro against ENTs. We used these ENT models to predict (i) the antivirals used in treating COVID-19, remdesivir and molnupiravir, inhibit ENT activity, and that (ii) remdesivir is an ENT substrate, as well as validating these predictions. In Phase II we plan on building on the foundation of Phase I and propose greatly expanding the ENT1 and ENT2 models through in vitro testing (at the University of Arizona) of >2000 approved drugs, natural products, and environmental toxicants as inhibitors of ENT transport. We will use these data to build and validate machine learning models using several algorithms, at Collaborations Pharmaceuticals, Inc. We will also test these models using external validation with additional molecules from vendor libraries and drug collections that are not in the model. In this process we will also build out the capabilities of MegaTransÒ to use 3D pharmacophore descriptors to incorporate molecular shape features and allow 3D searches. The return on investment of such a commercial tool would be that it could assist in the design and selection of more favorable compounds by avoiding transporters of interest (or, conversely, allow the targeting of specific transporters to increase uptake into organs). It could also identify compounds that are already approved that might present a drug-interaction risk. Predicting such behavior seen in vivo is ideal and will lead to the prioritization of compounds to test in vitro for potential drug-drug interactions. In summary, we propose generating large training sets for ENT1 and ENT2 transporters that we will use to generate an array of validated machine learning models of interest to drug discovery (with specific interest for those generating antivirals). MegaTransÒ will be a commercial product available for licensing by pharmaceutical, consumer product, agrochemical and regulatory groups, as well as fee-for-service consulting provided by Collaborations Pharmaceuticals, Inc.

概括 能够预测与重要人类转运蛋白的相互作用对新药设计具有价值 避免与它们相互作用并引起不良副作用的化合物。相反，一些药物转运蛋白 可用于将分子靶向特定的器官，这可能具有相当大的效用。了解 新型药物，天然产物和环境有毒物质及其与一系列的相互作用的相互作用 因此，这种转运蛋白对多个行业以及从监管的角度（例如， FDA，EPA和EMA）。能够以快速可靠的方式预测此类互动需要有效的 使用计算方法并从体外数据中学习，后者是一种正在迅速增长的资源。 在过去的20年中，我们一直在将不同的机器学习方法应用于 对药物转运蛋白进行建模，在许多情况下，为转运蛋白开发数据集很少 可用数据。现在，我们建议对几个转运蛋白进行此操作，这可能对药物发现很重要。在 第一阶段我们专注于OATP1B1（SLCO1B1），这是一种摄取转运蛋白，在很大程度上仅限于正弦 肝细胞的方面介导了各种结构无关化合物的运输，包括 几个临床上重要的药物家族的成员（包括他汀类药物，sartans和血管紧张素转化酶 （ACE）抑制剂）。我们在体外测试了一种针对一种底物的476种药物。然后，我们策划了这些数据并构建 使用多种机器学习方法以及模型评估指标的机器学习模型。这 使我们能够在称为Megatrans®的基于Web的软件工具中开发集成模型，该工具可实现 用户输入自己的复合结构，并为与运输蛋白的交互作用生成预测 使用几种不同的可视化兴趣，并可视化与每个模型的训练集的相似性 方法。此外，在第一阶段，我们还进行了初步数据策划，模型构建和验证 对于两个等效的核侧转运蛋白（ENT），ENT1和ENT2，存在于血液测试障碍物 （BTB），它们可以促进药物处置（例如，对于抗病毒药，从而有可能消除庇护所 可在精液中检测到的病毒位点）。我们生成了贝叶斯和药效团模型，并将其用于 预测多种化合物，然后在体外对ETS进行测试。我们使用这些ENT模型来预测 （i）用于治疗Covid-19，Remdesivir和molnupiravir的抗病毒药物抑制ENT活性，并且（ii） Remdesivir是一个替代基材，并且可以验证这些预测。在第二阶段，我们计划在 第一阶段和建议的基础通过体外测试大大扩展了ENT1和ENT2模型（在 亚利桑那大学的> 2000年批准的药物，天然产品和环境毒物作为抑制剂 Ent运输。我们将使用这些数据使用多种算法来构建和验证机器学习模型， 在合作Pharmaceuticals，Inc。中，我们还将使用外部验证测试这些模型 来自模型中的供应商库和药物收集的分子。在此过程中，我们还将建立 脱离Megatransò使用3D药效团描述符的能力来融合分子形状 功能并允许3D搜索。这种商业工具的投资回报是它可以协助 在设计和选择更有利化合物的过程中，避免了感兴趣的转运蛋白（或相反， 允许特定转运蛋白的靶向增加对器官的摄取）。它还可以识别化合物 已经被批准，可能会带来毒品交流风险。预测体内看到的这种行为是理想的 并将导致化合物的优先级，以在体外测试潜在的药物相互作用。总之， 我们建议为ENT1和ENT2转运蛋白生成大型训练集，我们将用于生成一个数组 受过验证的机器学习模型对药物发现的兴趣（对生成的人特别感兴趣 抗病毒药）。 Megatransò将是一款商业产品，可以由药品，消费者使用 产品，农业化学和监管组以及合作提供的费用服务咨询 Pharmaceuticals，Inc。